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Controller design for a class of nonlinear MIMO coupled system using multiple models and second level adaptation.

Vinay Kumar Pandey1, Indrani Kar1, Chitralekha Mahanta1

  • 1Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, 781039, India.

ISA Transactions
|May 17, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive control method for nonlinear multi-input multi-output (MIMO) systems. Real-time experiments on a twin rotor MIMO system (TRMS) validate the controller

Keywords:
Adaptive controlCross-couplingFeedback linearizationMIMO systemMultiple modelNonlinear systemSecond level adaptationTRMS

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Area of Science:

  • Control Engineering
  • Robotics
  • System Identification

Background:

  • Nonlinear multi-input multi-output (MIMO) coupled systems present significant control challenges.
  • Adaptive control strategies are crucial for systems with unknown or time-varying parameters.

Purpose of the Study:

  • To propose an adaptive control method with second-level adaptation for nonlinear MIMO systems.
  • To design and validate a state feedback controller using feedback linearization.
  • To demonstrate the controller's effectiveness on a twin rotor MIMO system (TRMS).

Main Methods:

  • Utilizing multiple estimation models for first-level parameter tuning.
  • Implementing a second-level adaptation for a unified controller parameter vector.
  • Employing feedback linearization for state feedback control design.
  • Using an Extended Kalman Filter (EKF) for state observation.

Main Results:

  • Successful real-time experimental validation on a twin rotor MIMO system (TRMS).
  • Demonstrated efficacy in both regulation and tracking tasks for pitch and yaw control.
  • Effective handling of nonlinear dynamics and parameter uncertainties.

Conclusions:

  • The proposed adaptive control method with second-level adaptation is effective for nonlinear MIMO systems.
  • The controller design, validated on the TRMS, shows robust performance for complex control objectives.
  • The integration of EKF enhances state estimation for improved control outcomes.